Course Outline
Understanding the Tools and a First Build
How AI Coding Tools Actually Work
Key topics include:
• Gaining a clear understanding of context windows and their inherent limitations
• Exploring statelessness and how AI models retain information during active sessions
• Mastering the Plan → Execute → Review workflow
• Identifying tasks where AI excels and areas where it may face challenges
• Adopting best practices for effective collaboration with AI assistants
The AI Coding Landscape
Key topics include:
• Surveying the current AI coding ecosystem
• Distinguishing between platforms such as Cursor, GitHub Copilot, and Claude Code
• Selecting the most appropriate model and tool for specific tasks
• Evaluating the strengths and limitations of various coding assistants
• Providing practical guidance for adopting AI tools within development teams
Prompt Anatomy
Key topics include:
• Identifying the essential components of a high-quality prompt
• Effectively providing context and clearly defining the task at hand
• Specifying desired output formats and necessary constraints
• Utilising common prompting frameworks and templates
• Applying techniques to enhance prompt quality and consistency
First Coding: Build From Scratch
Key topics include:
• Initiating project development from an empty directory
• Establishing the initial application structure and scaffolding
• Managing project dependencies and configuration settings
• Iteratively refining the generated code
• Testing and polishing the final solution
Existing Codebases, Personalisation and Review
Working in a Codebase
Key topics include:
• Navigating and comprehending unfamiliar codebases
• Leveraging AI tools to query and analyse existing projects
• Mapping out application structures and dependencies
• Generating documentation and technical summaries
• Streamlining the onboarding process for existing projects
Everyday Tasks: Fix, Feature and Test
Key topics include:
• Utilising AI tools to investigate and resolve bugs
• Implementing new features and system enhancements
• Writing and refining automated tests
• Validating AI-generated code and changes
• Boosting productivity in routine development tasks
Personalisation: What It Is
Key topics include:
• Understanding project-specific rules and configuration files
• Introduction to concepts like AGENTS.md and project memory
• Determining where and when personalisation mechanisms are applicable
• Following best practices for configuring AI assistants
• Overviewing advanced implementation strategies
Guardrails, Risks and Judgement
Key topics include:
• Critically reviewing and validating AI-generated code
• Understanding common failure modes and system limitations
• Recognising prompt injection threats and security risks
• Making informed decisions on which tasks to delegate to AI
• Applying human judgement and maintaining accountability in software development
Requirements
Participants do not need prior coding skills or experience with AI tools to join this course.
However, a basic familiarity with code or Git is beneficial.
A licensed account for Claude Code, Cursor, or Copilot is required.
Audience:
This course is tailored for individuals new to AI-assisted development, including non-coders, occasional programmers, and professionals in technical-adjacent fields such as QA, data, product, or operations. No prior development background is assumed.
Testimonials (2)
Considerations about privacy and policy
Angelo Stefano Soglia - Parkino srl
Course - Claude AI for Workflow Automation and Productivity
Learning how to prompt Claude and use it to digest all of the data I have available.
